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Article type: Research Article
Authors: Adamopoulos, Iannis E.a; * | Pataridis, Konstantinosb
Affiliations: [a] Washington University School of Medicine, Department of Pathology and Immunology, Campus Box 8118, 660 South Euclid Avenue, St. Louis, MO 63110, USA | [b] Department of Radiology, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
Correspondence: [*] Corresponding author: Iannis E. Adamopoulos, Washington University School of Medicine, Department of Pathology and Immunology, Campus Box 8118, 660 South Euclid Avenue, St. Louis, MO 63110, USA. Tel.: +1 314 454 7349; Fax: +1 314 454 5505; E-mail: [email protected] and [email protected].
Abstract: Bone destruction is a common feature of arthritis. Bone is resorbed by bone resorbing cells, termed osteoclasts. In medical research, quantification of the amount of bone resorbed areas is vital in understanding the resorptive capacity of the osteoclast under certain pathologic conditions, and its response to various treatments and pharmacological inhibitors. Validated image analysis algorithms and procedures, therefore, have become critical for elevating the quality of bone resorption assays results. As in all computational experimental methods in biology the pressure increases to make analysis transparent and reproducible. In this paper we present the novel software “OsteoPro” which has been designed specifically to address those issues. “OsteoPro” is a “turnkey” application that functions with minimal human interaction, by making use of morphological operations and blob analysis to classify structures according to their hue, saturation and size. In these experiments we have cultured osteoclasts on dentine slices, and the amount of bone resorption was analysed with the “OsteoPro” software using the techniques described in this paper. Finally “OsteoPro” is compared and contrast with other generic image processing suites, and further enhancements of the procedures used are also discussed.
Keywords: Image analysis, bone resorption, osteoclasts, bilateral filtering, blob analysis
DOI: 10.3233/JCM-2007-7201
Journal: Journal of Computational Methods in Sciences and Engineering, vol. 7, no. 2, pp. 87-91, 2007
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